Pérez-López Carlos, Ginebreda Antoni, Barcelo Damia, Tauler Roma
Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Department of Environmental Chemistry, Jordi Girona 18-26, Barcelona 08034, Spain.
Catalan Institute for Water Research (ICRA-CERCA), Emili Grahit 101, Parc Científic i Tecnològic de la Universitat de Girona, Edifici H2O, Girona 17003, Spain.
MethodsX. 2023 Apr 25;10:102199. doi: 10.1016/j.mex.2023.102199. eCollection 2023.
The Regions of Interest Multivariate curve Resolution (ROIMCR) methodology has gained significance for analyzing mass spectrometry data. The new SigSel package improves the ROIMCR methodology by providing a filtering step to reduce computational costs and to identify chemical compounds giving low-intensity signals. SigSel allows the visualization and assessment of ROIMCR results and filters out components resolved as interferences and background noise. This improves the analysis of complex mixtures and facilitates the identification of chemical compounds for statistical or chemometrics analysis. SigSel has been tested using metabolomics samples of mussels exposed to the sulfamethoxazole antibiotic. It begins by analyzing the data according to their charge state, eliminating signals considered background noise, and reducing the size of the datasets. In the ROIMCR analysis, the resolution of 30 ROIMCR components was achieved. After evaluating these components, 24 were ultimately selected explaining 99.05% of the total data variance. From ROIMCR results, chemical annotation is performed using different methods: •Generating a list of signals and reanalyzing them in a data-dependent analysis.•Comparing the ROIMCR resolved mass spectra to those stored in online repositories.•Searching MS signals of chemical compounds in the ROIMCR resolved spectra profiles.
感兴趣区域多元曲线分辨（ROIMCR）方法在分析质谱数据方面具有重要意义。新的SigSel软件包通过提供一个过滤步骤来改进ROIMCR方法，以降低计算成本并识别产生低强度信号的化合物。SigSel允许对ROIMCR结果进行可视化和评估，并滤除被解析为干扰和背景噪声的成分。这改善了对复杂混合物的分析，并便于识别用于统计或化学计量学分析的化合物。SigSel已使用暴露于磺胺甲恶唑抗生素的贻贝代谢组学样本进行了测试。它首先根据电荷状态分析数据，消除被视为背景噪声的信号，并减小数据集的大小。在ROIMCR分析中，实现了30个ROIMCR成分的分辨。在评估这些成分后，最终选择了24个，它们解释了总数据方差的99.05%。根据ROIMCR结果，使用不同方法进行化学注释：•生成信号列表并在数据依赖分析中重新分析它们。•将ROIMCR分辨的质谱与存储在在线数据库中的质谱进行比较。•在ROIMCR分辨的光谱图中搜索化合物的质谱信号。
